I'm expecting three big leaps in the next 10 years (in order):
1) Generative algorithms reach a level where the content is indistinguishable from human-generated content. Compute: performed on mega-clusters such as used by DALL-E, GPT-3, PaLM.
2) DALL-E/GPT-3/PaLM-level generative algorithms are able to run on personal hardware (phone, laptop)
3) Generative algorithms are able to fine-tune/train on personal hardware
Right now the algorithms are moving much faster than the hardware, hence why we are seeing large language models and gigantic generative models such as DALL-E 2. In time the hardware will catch up. For the next few years, applications of these models will be restricted by the fact that they're only accessible through API calls to mega-clusters run by Google, OpenAI, etc.
In time the hardware will improve and architectures will become more compute efficient to the point where we can achieve "human-level" (loosely defined) on personal hardware. Real-time generative content will change the way we consume content entirely, especially in the context of AR. Augmenting our view of the physical world with an infinite number of different filters will create infinite use cases for AR.
On the large language model side, once LLMs exceed our ability to comprehend libraries of documents, that will change the way we work, the way we perform science and many other things. Imagine querying your team's entire library of documents, design reviews and code with prompts such as: "Why was the load balancer for X service designed in this way?".